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  <a class="reference internal" href="%E7%AC%AC%E4%B8%80%E7%AB%A0/index.html">
   第一章：PyTorch的简介和安装
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     1.1 PyTorch简介
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     1.2 PyTorch的安装
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     1.3 PyTorch相关资源
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   第二章：PyTorch基础知识
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     2.1 张量
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     2.2 自动求导
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     2.3 并行计算简介
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   第三章：PyTorch的主要组成模块
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     3.1 思考：完成深度学习的必要部分
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     3.2 基本配置
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     3.3 数据读入
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     3.4 模型构建
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     3.5 模型初始化
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     3.6 损失函数
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     3.7 训练和评估
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     3.8 可视化
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     3.9 Pytorch优化器
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   第四章：PyTorch基础实战
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     基础实战——FashionMNIST时装分类
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   第五章：PyTorch模型定义
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     5.1 PyTorch模型定义的方式
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     5.2 利用模型块快速搭建复杂网络
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     5.3 PyTorch修改模型
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     5.4 PyTorch模型保存与读取
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   第六章：PyTorch进阶训练技巧
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     6.1 自定义损失函数
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     6.2 动态调整学习率
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     6.3 模型微调-torchvision
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     6.3 模型微调 - timm
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     6.4 半精度训练
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     6.5 数据增强-imgaug
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     6.6 使用argparse进行调参
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    <a class="reference internal" href="%E7%AC%AC%E5%85%AD%E7%AB%A0/PyTorch%E6%A8%A1%E5%9E%8B%E5%AE%9A%E4%B9%89%E4%B8%8E%E8%BF%9B%E9%98%B6%E8%AE%AD%E7%BB%83%E6%8A%80%E5%B7%A7.html">
     PyTorch模型定义与进阶训练技巧
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   第七章：PyTorch可视化
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     7.1 可视化网络结构
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     7.2 CNN可视化
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     7.3 使用TensorBoard可视化训练过程
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   第八章：PyTorch生态简介
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     8.1 本章简介
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     8.2 torchvision
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     8.3 PyTorchVideo简介
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     8.4 torchtext简介
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     transforms实战
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  <section class="tex2jax_ignore mathjax_ignore" id="pytorch">
<h1>深入浅出PyTorch<a class="headerlink" href="#pytorch" title="永久链接至标题">#</a></h1>
<p>PyTorch是利用深度学习进行数据科学研究的重要工具，在灵活性、可读性和性能上都具备相当的优势，近年来已成为学术界实现深度学习算法最常用的框架。</p>
<p>考虑到PyTorch的学习兼具理论储备和动手训练，两手都要抓两手都要硬的特点，我们开发了《深入浅出PyTorch》课程，期望以组队学习的形式，帮助大家从入门到熟练掌握PyTorch工具，进而实现自己的深度学习算法。</p>
<p>我们的愿景是：通过组队学习，大家能够掌握由浅入深地PyTorch的基本知识和内容，经过自己的动手实践加深操作的熟练度。同时通过项目实战，充分锻炼编程能力，掌握PyTorch进行深度学习的基本流程，提升解决实际问题的能力。</p>
<p>学习的先修要求是，会使用Python编程，了解包括神经网络在内的机器学习算法，勤于动手实践。</p>
<p>《深入浅出PyTorch》是一个系列，一共有三个部分。已经上线的是本系列的第一、二部分，后续会不断更新《深入浅出PyTorch》（下），给出更贴合实际应用的实战案例。</p>
<div class="toctree-wrapper compound">
<p aria-level="2" class="caption" role="heading"><span class="caption-text">目录</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="%E7%AC%AC%E4%B8%80%E7%AB%A0/index.html">第一章：PyTorch的简介和安装</a><ul>
<li class="toctree-l2"><a class="reference internal" href="%E7%AC%AC%E4%B8%80%E7%AB%A0/1.1%20PyTorch%E7%AE%80%E4%BB%8B.html">1.1 PyTorch简介</a></li>
<li class="toctree-l2"><a class="reference internal" href="%E7%AC%AC%E4%B8%80%E7%AB%A0/1.2%20PyTorch%E7%9A%84%E5%AE%89%E8%A3%85.html">1.2 PyTorch的安装</a></li>
<li class="toctree-l2"><a class="reference internal" href="%E7%AC%AC%E4%B8%80%E7%AB%A0/1.3%20PyTorch%E7%9B%B8%E5%85%B3%E8%B5%84%E6%BA%90.html">1.3 PyTorch相关资源</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="%E7%AC%AC%E4%BA%8C%E7%AB%A0/index.html">第二章：PyTorch基础知识</a><ul>
<li class="toctree-l2"><a class="reference internal" href="%E7%AC%AC%E4%BA%8C%E7%AB%A0/2.1%20%E5%BC%A0%E9%87%8F.html">2.1 张量</a></li>
<li class="toctree-l2"><a class="reference internal" href="%E7%AC%AC%E4%BA%8C%E7%AB%A0/2.2%20%E8%87%AA%E5%8A%A8%E6%B1%82%E5%AF%BC.html">2.2 自动求导</a></li>
<li class="toctree-l2"><a class="reference internal" href="%E7%AC%AC%E4%BA%8C%E7%AB%A0/2.3%20%E5%B9%B6%E8%A1%8C%E8%AE%A1%E7%AE%97%E7%AE%80%E4%BB%8B.html">2.3 并行计算简介</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="%E7%AC%AC%E4%B8%89%E7%AB%A0/index.html">第三章：PyTorch的主要组成模块</a><ul>
<li class="toctree-l2"><a class="reference internal" href="%E7%AC%AC%E4%B8%89%E7%AB%A0/3.1%20%E6%80%9D%E8%80%83%EF%BC%9A%E5%AE%8C%E6%88%90%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E7%9A%84%E5%BF%85%E8%A6%81%E9%83%A8%E5%88%86.html">3.1 思考：完成深度学习的必要部分</a></li>
<li class="toctree-l2"><a class="reference internal" href="%E7%AC%AC%E4%B8%89%E7%AB%A0/3.2%20%E5%9F%BA%E6%9C%AC%E9%85%8D%E7%BD%AE.html">3.2 基本配置</a></li>
<li class="toctree-l2"><a class="reference internal" href="%E7%AC%AC%E4%B8%89%E7%AB%A0/3.3%20%E6%95%B0%E6%8D%AE%E8%AF%BB%E5%85%A5.html">3.3 数据读入</a></li>
<li class="toctree-l2"><a class="reference internal" href="%E7%AC%AC%E4%B8%89%E7%AB%A0/3.4%20%E6%A8%A1%E5%9E%8B%E6%9E%84%E5%BB%BA.html">3.4 模型构建</a></li>
<li class="toctree-l2"><a class="reference internal" href="%E7%AC%AC%E4%B8%89%E7%AB%A0/3.5%20%E6%A8%A1%E5%9E%8B%E5%88%9D%E5%A7%8B%E5%8C%96.html">3.5 模型初始化</a></li>
<li class="toctree-l2"><a class="reference internal" href="%E7%AC%AC%E4%B8%89%E7%AB%A0/3.6%20%E6%8D%9F%E5%A4%B1%E5%87%BD%E6%95%B0.html">3.6 损失函数</a></li>
<li class="toctree-l2"><a class="reference internal" href="%E7%AC%AC%E4%B8%89%E7%AB%A0/3.7%20%E8%AE%AD%E7%BB%83%E4%B8%8E%E8%AF%84%E4%BC%B0.html">3.7 训练和评估</a></li>
<li class="toctree-l2"><a class="reference internal" href="%E7%AC%AC%E4%B8%89%E7%AB%A0/3.8%20%E5%8F%AF%E8%A7%86%E5%8C%96.html">3.8 可视化</a></li>
<li class="toctree-l2"><a class="reference internal" href="%E7%AC%AC%E4%B8%89%E7%AB%A0/3.9%20%E4%BC%98%E5%8C%96%E5%99%A8.html">3.9 Pytorch优化器</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="%E7%AC%AC%E5%9B%9B%E7%AB%A0/index.html">第四章：PyTorch基础实战</a><ul>
<li class="toctree-l2"><a class="reference internal" href="%E7%AC%AC%E5%9B%9B%E7%AB%A0/%E5%9F%BA%E7%A1%80%E5%AE%9E%E6%88%98%E2%80%94%E2%80%94FashionMNIST%E6%97%B6%E8%A3%85%E5%88%86%E7%B1%BB.html">基础实战——FashionMNIST时装分类</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="%E7%AC%AC%E4%BA%94%E7%AB%A0/index.html">第五章：PyTorch模型定义</a><ul>
<li class="toctree-l2"><a class="reference internal" href="%E7%AC%AC%E4%BA%94%E7%AB%A0/5.1%20PyTorch%E6%A8%A1%E5%9E%8B%E5%AE%9A%E4%B9%89%E7%9A%84%E6%96%B9%E5%BC%8F.html">5.1 PyTorch模型定义的方式</a></li>
<li class="toctree-l2"><a class="reference internal" href="%E7%AC%AC%E4%BA%94%E7%AB%A0/5.2%20%E5%88%A9%E7%94%A8%E6%A8%A1%E5%9E%8B%E5%9D%97%E5%BF%AB%E9%80%9F%E6%90%AD%E5%BB%BA%E5%A4%8D%E6%9D%82%E7%BD%91%E7%BB%9C.html">5.2 利用模型块快速搭建复杂网络</a></li>
<li class="toctree-l2"><a class="reference internal" href="%E7%AC%AC%E4%BA%94%E7%AB%A0/5.3%20PyTorch%E4%BF%AE%E6%94%B9%E6%A8%A1%E5%9E%8B.html">5.3 PyTorch修改模型</a></li>
<li class="toctree-l2"><a class="reference internal" href="%E7%AC%AC%E4%BA%94%E7%AB%A0/5.4%20PyTorh%E6%A8%A1%E5%9E%8B%E4%BF%9D%E5%AD%98%E4%B8%8E%E8%AF%BB%E5%8F%96.html">5.4 PyTorch模型保存与读取</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="%E7%AC%AC%E5%85%AD%E7%AB%A0/index.html">第六章：PyTorch进阶训练技巧</a><ul>
<li class="toctree-l2"><a class="reference internal" href="%E7%AC%AC%E5%85%AD%E7%AB%A0/6.1%20%E8%87%AA%E5%AE%9A%E4%B9%89%E6%8D%9F%E5%A4%B1%E5%87%BD%E6%95%B0.html">6.1 自定义损失函数</a></li>
<li class="toctree-l2"><a class="reference internal" href="%E7%AC%AC%E5%85%AD%E7%AB%A0/6.2%20%E5%8A%A8%E6%80%81%E8%B0%83%E6%95%B4%E5%AD%A6%E4%B9%A0%E7%8E%87.html">6.2 动态调整学习率</a></li>
<li class="toctree-l2"><a class="reference internal" href="%E7%AC%AC%E5%85%AD%E7%AB%A0/6.3%20%E6%A8%A1%E5%9E%8B%E5%BE%AE%E8%B0%83-torchvision.html">6.3 模型微调-torchvision</a></li>
<li class="toctree-l2"><a class="reference internal" href="%E7%AC%AC%E5%85%AD%E7%AB%A0/6.3%20%E6%A8%A1%E5%9E%8B%E5%BE%AE%E8%B0%83-timm.html">6.3 模型微调 - timm</a></li>
<li class="toctree-l2"><a class="reference internal" href="%E7%AC%AC%E5%85%AD%E7%AB%A0/6.4%20%E5%8D%8A%E7%B2%BE%E5%BA%A6%E8%AE%AD%E7%BB%83.html">6.4 半精度训练</a></li>
<li class="toctree-l2"><a class="reference internal" href="%E7%AC%AC%E5%85%AD%E7%AB%A0/6.5%20%E6%95%B0%E6%8D%AE%E5%A2%9E%E5%BC%BA-imgaug.html">6.5 数据增强-imgaug</a></li>
<li class="toctree-l2"><a class="reference internal" href="%E7%AC%AC%E5%85%AD%E7%AB%A0/6.6%20%E4%BD%BF%E7%94%A8argparse%E8%BF%9B%E8%A1%8C%E8%B0%83%E5%8F%82.html">6.6 使用argparse进行调参</a></li>
<li class="toctree-l2"><a class="reference internal" href="%E7%AC%AC%E5%85%AD%E7%AB%A0/PyTorch%E6%A8%A1%E5%9E%8B%E5%AE%9A%E4%B9%89%E4%B8%8E%E8%BF%9B%E9%98%B6%E8%AE%AD%E7%BB%83%E6%8A%80%E5%B7%A7.html">PyTorch模型定义与进阶训练技巧</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="%E7%AC%AC%E4%B8%83%E7%AB%A0/index.html">第七章：PyTorch可视化</a><ul>
<li class="toctree-l2"><a class="reference internal" href="%E7%AC%AC%E4%B8%83%E7%AB%A0/7.1%20%E5%8F%AF%E8%A7%86%E5%8C%96%E7%BD%91%E7%BB%9C%E7%BB%93%E6%9E%84.html">7.1 可视化网络结构</a></li>
<li class="toctree-l2"><a class="reference internal" href="%E7%AC%AC%E4%B8%83%E7%AB%A0/7.2%20CNN%E5%8D%B7%E7%A7%AF%E5%B1%82%E5%8F%AF%E8%A7%86%E5%8C%96.html">7.2 CNN可视化</a></li>
<li class="toctree-l2"><a class="reference internal" href="%E7%AC%AC%E4%B8%83%E7%AB%A0/7.3%20%E4%BD%BF%E7%94%A8TensorBoard%E5%8F%AF%E8%A7%86%E5%8C%96%E8%AE%AD%E7%BB%83%E8%BF%87%E7%A8%8B.html">7.3 使用TensorBoard可视化训练过程</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="%E7%AC%AC%E5%85%AB%E7%AB%A0/index.html">第八章：PyTorch生态简介</a><ul>
<li class="toctree-l2"><a class="reference internal" href="%E7%AC%AC%E5%85%AB%E7%AB%A0/8.1%20%E6%9C%AC%E7%AB%A0%E7%AE%80%E4%BB%8B.html">8.1 本章简介</a></li>
<li class="toctree-l2"><a class="reference internal" href="%E7%AC%AC%E5%85%AB%E7%AB%A0/8.2%20%E5%9B%BE%E5%83%8F%20-%20torchvision.html">8.2 torchvision</a></li>
<li class="toctree-l2"><a class="reference internal" href="%E7%AC%AC%E5%85%AB%E7%AB%A0/8.3%20%E8%A7%86%E9%A2%91%20-%20PyTorchVideo.html">8.3 PyTorchVideo简介</a></li>
<li class="toctree-l2"><a class="reference internal" href="%E7%AC%AC%E5%85%AB%E7%AB%A0/8.4%20%E6%96%87%E6%9C%AC%20-%20torchtext.html">8.4 torchtext简介</a></li>
<li class="toctree-l2"><a class="reference internal" href="%E7%AC%AC%E5%85%AB%E7%AB%A0/transforms%E5%AE%9E%E6%93%8D.html">transforms实战</a></li>
</ul>
</li>
</ul>
</div>
<section id="id1">
<h2>人员安排<a class="headerlink" href="#id1" title="永久链接至标题">#</a></h2>
<table class="colwidths-auto table">
<thead>
<tr class="row-odd"><th class="text-align:left head"><p>成员</p></th>
<th class="text-align:left head"><p>个人简介</p></th>
<th class="text-align:center head"><p>个人主页</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td class="text-align:left"><p>牛志康</p></td>
<td class="text-align:left"><p>DataWhale成员，西安电子科技大学本科生</p></td>
<td class="text-align:center"><p><a class="reference external" href="https://www.zhihu.com/people/obeah-82">个人主页</a></p></td>
</tr>
<tr class="row-odd"><td class="text-align:left"><p>李嘉骐</p></td>
<td class="text-align:left"><p>DataWhale成员，清华大学研究生</p></td>
<td class="text-align:center"><p><a class="reference external" href="https://www.zhihu.com/people/li-jia-qi-16-9/posts">个人主页</a></p></td>
</tr>
<tr class="row-even"><td class="text-align:left"><p>刘洋</p></td>
<td class="text-align:left"><p>Datawhale成员，中国科学院数学与系统科学研究所研究生</p></td>
<td class="text-align:center"><p><a class="reference external" href="https://www.zhihu.com/people/ming-ren-19-34/asks">个人主页</a></p></td>
</tr>
<tr class="row-odd"><td class="text-align:left"><p>陈安东</p></td>
<td class="text-align:left"><p>Datawhale成员，中央民族大学研究生</p></td>
<td class="text-align:center"><p><a class="reference external" href="https://andongblue.github.io/chenandong.github.io/">个人主页</a></p></td>
</tr>
</tbody>
</table>
<ul class="simple">
<li><p>教程贡献情况（已上线课程内容）：</p>
<ul>
<li><p>牛志康：第一章；第三章；第六章；第七章；第八章；第九章；在线内容管理</p></li>
<li><p>李嘉骐：第三章；第四章；第五章；第六章；第七章；第八章；内容整合</p></li>
<li><p>刘洋  ：第二章；第三章</p></li>
<li><p>陈安东：第二章；第三章；第七章</p></li>
</ul>
</li>
</ul>
</section>
<section id="id2">
<h2>更新计划<a class="headerlink" href="#id2" title="永久链接至标题">#</a></h2>
<table class="colwidths-auto table">
<thead>
<tr class="row-odd"><th class="text-align:left head"><p>内容</p></th>
<th class="text-align:left head"><p>更新时间</p></th>
<th class="text-align:center head"><p>内容</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td class="text-align:left"><p>模型初始化</p></td>
<td class="text-align:left"><p>✅</p></td>
<td class="text-align:center"><p><code class="docutils literal notranslate"><span class="pre">torch.nn.init</span></code>的使用</p></td>
</tr>
<tr class="row-odd"><td class="text-align:left"><p>visdom可视化</p></td>
<td class="text-align:left"><p>2022.4.5</p></td>
<td class="text-align:center"><p><code class="docutils literal notranslate"><span class="pre">Visdom</span></code>的使用</p></td>
</tr>
<tr class="row-even"><td class="text-align:left"><p>apex</p></td>
<td class="text-align:left"><p>2022.5.4</p></td>
<td class="text-align:center"><p>apex的简介和使用</p></td>
</tr>
<tr class="row-odd"><td class="text-align:left"><p>超参数的保存</p></td>
<td class="text-align:left"><p>✅</p></td>
<td class="text-align:center"><p>介绍argparse与yaml</p></td>
</tr>
<tr class="row-even"><td class="text-align:left"><p>onnx</p></td>
<td class="text-align:left"><p>2022.5.31</p></td>
<td class="text-align:center"><p>讲述ONNX格式和实战例子</p></td>
</tr>
<tr class="row-odd"><td class="text-align:left"><p>模型部署</p></td>
<td class="text-align:left"><p></p></td>
<td class="text-align:center"><p>Flask部署PyTorch模型</p></td>
</tr>
<tr class="row-even"><td class="text-align:left"><p>TorchScript</p></td>
<td class="text-align:left"><p></p></td>
<td class="text-align:center"><p>TorchScript</p></td>
</tr>
<tr class="row-odd"><td class="text-align:left"><p>数据增强</p></td>
<td class="text-align:left"><p>✅</p></td>
<td class="text-align:center"><p>imgaug的使用</p></td>
</tr>
<tr class="row-even"><td class="text-align:left"><p>并行训练</p></td>
<td class="text-align:left"><p></p></td>
<td class="text-align:center"><p>并行训练</p></td>
</tr>
<tr class="row-odd"><td class="text-align:left"><p>模型预训练 - torchhub</p></td>
<td class="text-align:left"><p>2022.4.16</p></td>
<td class="text-align:center"><p>torchhub的简介和使用方法</p></td>
</tr>
<tr class="row-even"><td class="text-align:left"><p>模型预训练 - timm</p></td>
<td class="text-align:left"><p>✅</p></td>
<td class="text-align:center"><p>timm预训练模型的使用方法</p></td>
</tr>
<tr class="row-odd"><td class="text-align:left"><p>模型预训练 - openmmlab</p></td>
<td class="text-align:left"><p>2022.4.27</p></td>
<td class="text-align:center"><p>openmmlab系列的使用</p></td>
</tr>
<tr class="row-even"><td class="text-align:left"><p>目标检测 - yolo系列</p></td>
<td class="text-align:left"><p></p></td>
<td class="text-align:center"><p>Yolo系列介绍与trick实现</p></td>
</tr>
<tr class="row-odd"><td class="text-align:left"><p>目标检测 - SSD</p></td>
<td class="text-align:left"><p></p></td>
<td class="text-align:center"><p>SSD的简介和实现</p></td>
</tr>
<tr class="row-even"><td class="text-align:left"><p>目标检测 - RCNN系列</p></td>
<td class="text-align:left"><p></p></td>
<td class="text-align:center"><p>Fast-RCNN &amp; Mask-RCNN</p></td>
</tr>
<tr class="row-odd"><td class="text-align:left"><p>目标检测 - DETR</p></td>
<td class="text-align:left"><p></p></td>
<td class="text-align:center"><p>DETR的实现</p></td>
</tr>
<tr class="row-even"><td class="text-align:left"><p>图像分类 - GoogLeNet</p></td>
<td class="text-align:left"><p>2022.5.11</p></td>
<td class="text-align:center"><p>GoogLeNet的介绍与实现</p></td>
</tr>
<tr class="row-odd"><td class="text-align:left"><p>图像分类 - Vision transformer</p></td>
<td class="text-align:left"><p>2022.5.18</p></td>
<td class="text-align:center"><p>Vit介绍与实现</p></td>
</tr>
<tr class="row-even"><td class="text-align:left"><p>图像分类 - MobileNet系列</p></td>
<td class="text-align:left"><p>2022.4月</p></td>
<td class="text-align:center"><p>MobileNet系列介绍与实现</p></td>
</tr>
<tr class="row-odd"><td class="text-align:left"><p>图像分类 - GhostNet</p></td>
<td class="text-align:left"><p>2022.4月</p></td>
<td class="text-align:center"><p>GhostNet代码讲解</p></td>
</tr>
<tr class="row-even"><td class="text-align:left"><p>生成式对抗网络 - 生成手写数字实战</p></td>
<td class="text-align:left"><p>2022.5.25</p></td>
<td class="text-align:center"><p>生成数字并可视化</p></td>
</tr>
<tr class="row-odd"><td class="text-align:left"><p>生成式对抗网络 - DCGAN</p></td>
<td class="text-align:left"><p></p></td>
<td class="text-align:center"><p></p></td>
</tr>
<tr class="row-even"><td class="text-align:left"><p>风格迁移 - StyleGAN</p></td>
<td class="text-align:left"><p></p></td>
<td class="text-align:center"><p></p></td>
</tr>
<tr class="row-odd"><td class="text-align:left"><p>生成网络 - VAE</p></td>
<td class="text-align:left"><p></p></td>
<td class="text-align:center"><p></p></td>
</tr>
<tr class="row-even"><td class="text-align:left"><p>图像分割 Deeplab系列</p></td>
<td class="text-align:left"><p></p></td>
<td class="text-align:center"><p>Deeplab系列代码讲解</p></td>
</tr>
<tr class="row-odd"><td class="text-align:left"><p>自然语言处理 LSTM</p></td>
<td class="text-align:left"><p></p></td>
<td class="text-align:center"><p>LSTM情感分析实战</p></td>
</tr>
<tr class="row-even"><td class="text-align:left"><p>自然语言处理 RNN</p></td>
<td class="text-align:left"><p></p></td>
<td class="text-align:center"><p>RNN名字分类</p></td>
</tr>
<tr class="row-odd"><td class="text-align:left"><p>自然语言处理 Transformer</p></td>
<td class="text-align:left"><p></p></td>
<td class="text-align:center"><p></p></td>
</tr>
<tr class="row-even"><td class="text-align:left"><p>自然语言处理 BERT</p></td>
<td class="text-align:left"><p></p></td>
<td class="text-align:center"><p></p></td>
</tr>
<tr class="row-odd"><td class="text-align:left"><p>视频</p></td>
<td class="text-align:left"><p></p></td>
<td class="text-align:center"><p>待定</p></td>
</tr>
<tr class="row-even"><td class="text-align:left"><p>音频</p></td>
<td class="text-align:left"><p></p></td>
<td class="text-align:center"><p>待定</p></td>
</tr>
<tr class="row-odd"><td class="text-align:left"><p>自定义CUDA扩展和算子</p></td>
<td class="text-align:left"><p></p></td>
<td class="text-align:center"><p></p></td>
</tr>
</tbody>
</table>
</section>
<section id="id3">
<h2>课程编排与使用方法<a class="headerlink" href="#id3" title="永久链接至标题">#</a></h2>
<p>部分章节直播讲解请观看B站回放（持续更新）：<a class="reference external" href="https://www.bilibili.com/video/BV1L44y1472Z">bilibili链接</a></p>
<ul>
<li><p>课程编排：
深入浅出PyTorch分为三个阶段：PyTorch深度学习基础知识、PyTorch进阶操作、PyTorch案例分析。</p></li>
<li><p>使用方法:</p>
<p>我们的课程内容都以markdown格式或Jupyter notebook的形式保存在本仓库内。除了多看加深课程内容的理解外，最重要的还是动手练习、练习、练习</p>
</li>
<li><p>组队学习安排:</p>
<p>第一部分：第一章到第四章，学习周期：10天；</p>
<p>第二部分：第五章到第八章，学习周期：11天</p>
</li>
</ul>
</section>
<section id="id4">
<h2>鸣谢与反馈<a class="headerlink" href="#id4" title="永久链接至标题">#</a></h2>
<ul class="simple">
<li><p>非常感谢DataWhale成员 叶前坤 &#64;<a class="reference external" href="https://github.com/PureBuckwheat">PureBuckwheat</a> 和 胡锐锋 &#64;<a class="reference external" href="https://github.com/Relph1119">Relph1119</a> 对文档的细致校对！</p></li>
<li><p>如果有任何想法可以联系我们DataWhale</p></li>
</ul>
<p><img alt="二维码" src="_images/qrcode.jpeg" /></p>
</section>
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